{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 案例分析\n",
    "# 武汉 - 湖北（除武汉外） - 湖北以外 疫情对比"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 载入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import utils   # some convenient functions\n",
    "import matplotlib.font_manager as mfm\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatic pdb calling has been turned OFF\n"
     ]
    }
   ],
   "source": [
    "%pdb off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last update:  2020-05-01 11:17:19\n",
      "Data date range:  2020-01-22 to 2020-05-01\n",
      "Number of rows in raw data:  159230\n"
     ]
    }
   ],
   "source": [
    "raw = utils.load_chinese_raw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>continentName</th>\n",
       "      <th>continentEnglishName</th>\n",
       "      <th>countryName</th>\n",
       "      <th>countryEnglishName</th>\n",
       "      <th>province_name</th>\n",
       "      <th>provinceEnglishName</th>\n",
       "      <th>province_zipCode</th>\n",
       "      <th>province_confirmed</th>\n",
       "      <th>province_suspected</th>\n",
       "      <th>province_cured</th>\n",
       "      <th>province_dead</th>\n",
       "      <th>update_time</th>\n",
       "      <th>city_name</th>\n",
       "      <th>cityEnglishName</th>\n",
       "      <th>city_zipCode</th>\n",
       "      <th>city_confirmed</th>\n",
       "      <th>city_suspected</th>\n",
       "      <th>city_cured</th>\n",
       "      <th>city_dead</th>\n",
       "      <th>update_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>亚洲</td>\n",
       "      <td>Asia</td>\n",
       "      <td>印度</td>\n",
       "      <td>India</td>\n",
       "      <td>印度</td>\n",
       "      <td>India</td>\n",
       "      <td>953003</td>\n",
       "      <td>33610</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8373</td>\n",
       "      <td>1075</td>\n",
       "      <td>2020-05-01 11:17:19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>北美洲</td>\n",
       "      <td>North America</td>\n",
       "      <td>美国</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>美国</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>971002</td>\n",
       "      <td>1069424</td>\n",
       "      <td>0.0</td>\n",
       "      <td>153947</td>\n",
       "      <td>62996</td>\n",
       "      <td>2020-05-01 11:04:57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>大洋洲</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>澳大利亚</td>\n",
       "      <td>Australia</td>\n",
       "      <td>澳大利亚</td>\n",
       "      <td>Australia</td>\n",
       "      <td>990001</td>\n",
       "      <td>6762</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5600</td>\n",
       "      <td>92</td>\n",
       "      <td>2020-05-01 11:04:57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>欧洲</td>\n",
       "      <td>Europe</td>\n",
       "      <td>卢森堡</td>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>卢森堡</td>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>961004</td>\n",
       "      <td>3784</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3134</td>\n",
       "      <td>90</td>\n",
       "      <td>2020-05-01 11:04:57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>亚洲</td>\n",
       "      <td>Asia</td>\n",
       "      <td>新加坡</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>新加坡</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>952009</td>\n",
       "      <td>16169</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1244</td>\n",
       "      <td>15</td>\n",
       "      <td>2020-05-01 10:18:59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-05-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  continentName continentEnglishName countryName        countryEnglishName  \\\n",
       "0            亚洲                 Asia          印度                     India   \n",
       "1           北美洲        North America          美国  United States of America   \n",
       "2           大洋洲              Oceania        澳大利亚                 Australia   \n",
       "3            欧洲               Europe         卢森堡                Luxembourg   \n",
       "4            亚洲                 Asia         新加坡                 Singapore   \n",
       "\n",
       "  province_name       provinceEnglishName  province_zipCode  \\\n",
       "0            印度                     India            953003   \n",
       "1            美国  United States of America            971002   \n",
       "2          澳大利亚                 Australia            990001   \n",
       "3           卢森堡                Luxembourg            961004   \n",
       "4           新加坡                 Singapore            952009   \n",
       "\n",
       "   province_confirmed  province_suspected  province_cured  province_dead  \\\n",
       "0               33610                 0.0            8373           1075   \n",
       "1             1069424                 0.0          153947          62996   \n",
       "2                6762                 0.0            5600             92   \n",
       "3                3784                 0.0            3134             90   \n",
       "4               16169                 0.0            1244             15   \n",
       "\n",
       "          update_time city_name cityEnglishName  city_zipCode  city_confirmed  \\\n",
       "0 2020-05-01 11:17:19       NaN             NaN           NaN             NaN   \n",
       "1 2020-05-01 11:04:57       NaN             NaN           NaN             NaN   \n",
       "2 2020-05-01 11:04:57       NaN             NaN           NaN             NaN   \n",
       "3 2020-05-01 11:04:57       NaN             NaN           NaN             NaN   \n",
       "4 2020-05-01 10:18:59       NaN             NaN           NaN             NaN   \n",
       "\n",
       "   city_suspected  city_cured  city_dead update_date  \n",
       "0             NaN         NaN        NaN  2020-05-01  \n",
       "1             NaN         NaN        NaN  2020-05-01  \n",
       "2             NaN         NaN        NaN  2020-05-01  \n",
       "3             NaN         NaN        NaN  2020-05-01  \n",
       "4             NaN         NaN        NaN  2020-05-01  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>continentName</th>\n",
       "      <th>continentEnglishName</th>\n",
       "      <th>countryName</th>\n",
       "      <th>countryEnglishName</th>\n",
       "      <th>province_name</th>\n",
       "      <th>provinceEnglishName</th>\n",
       "      <th>province_zipCode</th>\n",
       "      <th>province_confirmed</th>\n",
       "      <th>province_suspected</th>\n",
       "      <th>province_cured</th>\n",
       "      <th>province_dead</th>\n",
       "      <th>update_time</th>\n",
       "      <th>city_name</th>\n",
       "      <th>cityEnglishName</th>\n",
       "      <th>city_zipCode</th>\n",
       "      <th>city_confirmed</th>\n",
       "      <th>city_suspected</th>\n",
       "      <th>city_cured</th>\n",
       "      <th>city_dead</th>\n",
       "      <th>update_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>93104</th>\n",
       "      <td>亚洲</td>\n",
       "      <td>Asia</td>\n",
       "      <td>中国</td>\n",
       "      <td>China</td>\n",
       "      <td>湖北省</td>\n",
       "      <td>Hubei</td>\n",
       "      <td>420000</td>\n",
       "      <td>67217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36208</td>\n",
       "      <td>2835</td>\n",
       "      <td>2020-03-03 19:41:02</td>\n",
       "      <td>武汉</td>\n",
       "      <td>Wuhan</td>\n",
       "      <td>420100.0</td>\n",
       "      <td>49426.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23068.0</td>\n",
       "      <td>2252.0</td>\n",
       "      <td>2020-03-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93240</th>\n",
       "      <td>亚洲</td>\n",
       "      <td>Asia</td>\n",
       "      <td>中国</td>\n",
       "      <td>China</td>\n",
       "      <td>湖北省</td>\n",
       "      <td>Hubei</td>\n",
       "      <td>420000</td>\n",
       "      <td>67217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36203</td>\n",
       "      <td>2835</td>\n",
       "      <td>2020-03-03 18:09:01</td>\n",
       "      <td>武汉</td>\n",
       "      <td>Wuhan</td>\n",
       "      <td>420100.0</td>\n",
       "      <td>49426.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23063.0</td>\n",
       "      <td>2252.0</td>\n",
       "      <td>2020-03-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93503</th>\n",
       "      <td>亚洲</td>\n",
       "      <td>Asia</td>\n",
       "      <td>中国</td>\n",
       "      <td>China</td>\n",
       "      <td>湖北省</td>\n",
       "      <td>Hubei</td>\n",
       "      <td>420000</td>\n",
       "      <td>67217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36199</td>\n",
       "      <td>2834</td>\n",
       "      <td>2020-03-03 15:13:12</td>\n",
       "      <td>武汉</td>\n",
       "      <td>Wuhan</td>\n",
       "      <td>420100.0</td>\n",
       "      <td>49426.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23063.0</td>\n",
       "      <td>2251.0</td>\n",
       "      <td>2020-03-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94048</th>\n",
       "      <td>亚洲</td>\n",
       "      <td>Asia</td>\n",
       "      <td>中国</td>\n",
       "      <td>China</td>\n",
       "      <td>湖北省</td>\n",
       "      <td>Hubei</td>\n",
       "      <td>420000</td>\n",
       "      <td>67217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36167</td>\n",
       "      <td>2834</td>\n",
       "      <td>2020-03-03 08:27:46</td>\n",
       "      <td>武汉</td>\n",
       "      <td>Wuhan</td>\n",
       "      <td>420100.0</td>\n",
       "      <td>49315.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21349.0</td>\n",
       "      <td>2227.0</td>\n",
       "      <td>2020-03-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      continentName continentEnglishName countryName countryEnglishName  \\\n",
       "93104            亚洲                 Asia          中国              China   \n",
       "93240            亚洲                 Asia          中国              China   \n",
       "93503            亚洲                 Asia          中国              China   \n",
       "94048            亚洲                 Asia          中国              China   \n",
       "\n",
       "      province_name provinceEnglishName  province_zipCode  province_confirmed  \\\n",
       "93104           湖北省               Hubei            420000               67217   \n",
       "93240           湖北省               Hubei            420000               67217   \n",
       "93503           湖北省               Hubei            420000               67217   \n",
       "94048           湖北省               Hubei            420000               67217   \n",
       "\n",
       "       province_suspected  province_cured  province_dead         update_time  \\\n",
       "93104                 0.0           36208           2835 2020-03-03 19:41:02   \n",
       "93240                 0.0           36203           2835 2020-03-03 18:09:01   \n",
       "93503                 0.0           36199           2834 2020-03-03 15:13:12   \n",
       "94048                 0.0           36167           2834 2020-03-03 08:27:46   \n",
       "\n",
       "      city_name cityEnglishName  city_zipCode  city_confirmed  city_suspected  \\\n",
       "93104        武汉           Wuhan      420100.0         49426.0             0.0   \n",
       "93240        武汉           Wuhan      420100.0         49426.0             0.0   \n",
       "93503        武汉           Wuhan      420100.0         49426.0             0.0   \n",
       "94048        武汉           Wuhan      420100.0         49315.0             0.0   \n",
       "\n",
       "       city_cured  city_dead update_date  \n",
       "93104     23068.0     2252.0  2020-03-03  \n",
       "93240     23063.0     2252.0  2020-03-03  \n",
       "93503     23063.0     2251.0  2020-03-03  \n",
       "94048     21349.0     2227.0  2020-03-03  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wuhan_raw = raw[raw['city_name'] == '武汉']\n",
    "wuhan_raw[wuhan_raw['update_date'] == pd.to_datetime('2020-03-03')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last update:  2020-04-24 20:45:30\n",
      "Data date range:  2020-01-22 to 2020-04-24\n",
      "Number of rows in raw data:  150098\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Cannot mask with non-boolean array containing NA / NaN values",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-df123db8c355>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_chinese_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\CloudStation\\Projects\\nCov2019_analysis\\src\\utils.py\u001b[0m in \u001b[0;36mload_chinese_data\u001b[1;34m()\u001b[0m\n\u001b[0;32m    100\u001b[0m     \u001b[1;34m''' This includes some basic cleaning'''\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    101\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mload_chinese_raw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 102\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mrename_cities\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    103\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    104\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\CloudStation\\Projects\\nCov2019_analysis\\src\\utils.py\u001b[0m in \u001b[0;36mrename_cities\u001b[1;34m(snapshots)\u001b[0m\n\u001b[0;32m    181\u001b[0m     \u001b[0mFor\u001b[0m \u001b[0mnow\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mentries\u001b[0m \u001b[0mwill\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mignored\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcity_name\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mxxx\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxxx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mxxx\u001b[0m \u001b[0malready\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mcity_name\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    182\u001b[0m     '''\n\u001b[1;32m--> 183\u001b[1;33m     \u001b[0mdup_frm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msnapshots\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msnapshots\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'city_name'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontains\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'（'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    184\u001b[0m     \u001b[0mrename_dict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    185\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdup_frm\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\research\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2788\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2789\u001b[0m         \u001b[1;31m# Do we have a (boolean) 1d indexer?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2790\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_bool_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2791\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_bool_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2792\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\research\\lib\\site-packages\\pandas\\core\\common.py\u001b[0m in \u001b[0;36mis_bool_indexer\u001b[1;34m(key)\u001b[0m\n\u001b[0;32m    134\u001b[0m                 \u001b[0mna_msg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"Cannot mask with non-boolean array containing NA / NaN values\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    135\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 136\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mna_msg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    137\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    138\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Cannot mask with non-boolean array containing NA / NaN values"
     ]
    }
   ],
   "source": [
    "data = utils.load_chinese_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_frm = utils.aggDaily(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 把数据分成 武汉 -- 湖北（除武汉） -- 全国（除湖北） 三个区域各自整合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "wuhan = daily_frm[daily_frm['city_name'] == '武汉'].groupby('update_date').agg('sum')\n",
    "hubei_exWuhan = daily_frm[(daily_frm['province_name'] == '湖北省') & (daily_frm['city_name'] != '武汉')].groupby('update_date').agg('sum')\n",
    "china_exHubei = daily_frm[daily_frm['province_name'] != '湖北省'].groupby('update_date').agg('sum')\n",
    "\n",
    "contrast_frm = pd.merge(wuhan.add_suffix('_Wuhan'), hubei_exWuhan.add_suffix('_Hubei_exWuhan'), 'left', on='update_date')\n",
    "contrast_frm = pd.merge(contrast_frm, china_exHubei.add_suffix('_China_exHubei'), 'left', on='update_date')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 画图比较确诊人数和死亡人数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"None of [Index(['confirmed_Wuhan', 'confirmed_Hubei_exWuhan',\\n       'confirmed_China_exHubei'],\\n      dtype='object')] are in the [columns]\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-8b5880e8af8f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0max1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m211\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mcontrast_frm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'confirmed_'\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0msuffix\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0msuffix\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlocations\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgrid\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m13\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'-*'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'--*'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m':*'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0max1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0max1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'确诊人数对比'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfontproperties\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_FONT_PROP_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m15\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0max2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m212\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\research\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    831\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    832\u001b[0m                 \u001b[1;31m# don't overwrite\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 833\u001b[1;33m                 \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    834\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    835\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mABCSeries\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\research\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2804\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2805\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2806\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2807\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2808\u001b[0m         \u001b[1;31m# take() does not accept boolean indexers\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\research\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[1;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[0;32m   1550\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1551\u001b[0m         self._validate_read_indexer(\n\u001b[1;32m-> 1552\u001b[1;33m             \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mraise_missing\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1553\u001b[0m         )\n\u001b[0;32m   1554\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\research\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[1;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[0;32m   1637\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mmissing\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1638\u001b[0m                 \u001b[0maxis_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_axis_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1639\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"None of [{key}] are in the [{axis_name}]\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1640\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1641\u001b[0m             \u001b[1;31m# We (temporarily) allow for some missing keys with .loc, except in\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"None of [Index(['confirmed_Wuhan', 'confirmed_Hubei_exWuhan',\\n       'confirmed_China_exHubei'],\\n      dtype='object')] are in the [columns]\""
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "locations = ('Wuhan', 'Hubei_exWuhan', 'China_exHubei')\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(211)\n",
    "contrast_frm.plot(y=['confirmed_' + suffix for suffix in locations], grid=True, figsize=(13, 8), style=['-*', '--*', ':*'], ax=ax1)\n",
    "ax1.set_title('确诊人数对比', fontproperties=utils._FONT_PROP_, fontsize=15)\n",
    "ax2 = fig.add_subplot(212)\n",
    "contrast_frm.plot(y=['dead_' + suffix for suffix in locations], grid=True, figsize=(13, 8), style=['-*', '--*', ':*'], ax=ax2, sharex=True)\n",
    "ax2.set_title('死亡人数对比', fontproperties=utils._FONT_PROP_, fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图可以看出，虽然武汉，湖北除武汉，全国除湖北三个区域的确诊人数接近，但是死亡人数却有极大的差异：\n",
    "\n",
    "武汉 >> 湖北除武汉 >> 全国除湖北\n",
    "\n",
    "这是为什么呢？\n",
    "\n",
    "我们定义一个“简单死亡率” (Simple Death Rate)\n",
    "\n",
    "$$简单死亡率(t) \\equiv \\frac{死亡人数(t)}{确诊人数(t)}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "for loc in locations:\n",
    "    contrast_frm['simpleDeathRate_' + loc] = contrast_frm['dead_' + loc] / contrast_frm['confirmed_' + loc]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13bf14737b8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "contrast_frm.plot(y=['simpleDeathRate_' + loc for loc in locations], grid=True, figsize=(14, 6), style=['-*', '--*', ':*'],\n",
    "                 title='Simple Death Rate Comparison')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图可以看出，武汉死亡率 > 湖北（除武汉外）死亡率 > 全国其他地区死亡率\n",
    "\n",
    "这说明武汉死亡人数高不是因为确诊人数多造成的。但是我们知道，死亡人数应该比确诊人数有个滞后，会不会是这个滞后造成计算不准确呢？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lagDeathRate(df, location, lag):\n",
    "    confirmed = df['confirmed_' + location][:-(lag+1)].to_numpy()\n",
    "    if lag == 0:\n",
    "        v = df['dailyNew_dead_' + location].to_numpy() / df['dailyNew_confirmed_' + location].to_numpy()\n",
    "    else:\n",
    "        v = df['dailyNew_dead_' + location][lag:].to_numpy() / df['dailyNew_confirmed_' + location][:-lag].to_numpy()\n",
    "    return np.concatenate((np.array([np.nan] * (lag + 1)), v[1:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jian\\Anaconda3\\envs\\research\\lib\\site-packages\\ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in true_divide\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\Jian\\Anaconda3\\envs\\research\\lib\\site-packages\\ipykernel_launcher.py:5: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "for lag in range(0, 14, 2):\n",
    "    contrast_frm['lagDeath_' + str(lag) + '_Wuhan'] = lagDeathRate(contrast_frm, 'Wuhan', lag)\n",
    "    contrast_frm['lagDeath_' + str(lag) + '_Hubei_exWuhan'] = lagDeathRate(contrast_frm, 'Hubei_exWuhan', lag)\n",
    "    contrast_frm['lagDeath_' + str(lag) + '_China_exHubei'] = lagDeathRate(contrast_frm, 'China_exHubei', lag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13bf2df6630>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "contrast_frm.plot(y=['lagDeath_0_Wuhan', 'lagDeath_0_Hubei_exWuhan', 'lagDeath_0_China_exHubei'], figsize=(15, 6), style='-*', grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13bf1f2ada0>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lag = 2\n",
    "contrast_frm.plot(y=['lagDeath_' + str(lag) + '_Wuhan', 'lagDeath_' + str(lag) + '_Hubei_exWuhan', 'lagDeath_' + str(lag) + '_China_exHubei'], \n",
    "                  figsize=(15, 6), style='-*', grid=True, title='Lag ' + str(lag) + ' Death Rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "contrast_frm.to_csv('death.csv', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13bf22ca5c0>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lag = 8\n",
    "contrast_frm.plot(y=['lagDeath_' + str(lag) + '_Wuhan', 'lagDeath_' + str(lag) + '_Hubei_exWuhan', 'lagDeath_' + str(lag) + '_China_exHubei'], \n",
    "                  figsize=(15, 6), style='-*', grid=True, title='Lag ' + str(lag) + ' Death Rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
